A Novel Robust Adaptive Beamforming Algorithm Based on Total Least Squares and Compressed Sensing

Di YAO  Xin ZHANG  Qiang YANG  Weibo DENG  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.12   pp.3049-3053
Publication Date: 2017/12/01
Online ISSN: 1745-1337
Type of Manuscript: LETTER
Category: Digital Signal Processing
Keyword: 
covariance matrix construction,  array steering vector estimation,  compressed sensing,  total least squares,  

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Summary: 
An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.